{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}\\adsroot.itcs.umich.edu\home\hursre\Documents\Papers\Evangelical Signalling\Paper Drafts\JOP\Replication_Material\Log_of_Analysis_do.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}22 Aug 2022, 09:39:41

{com}. do "C:\Users\hursre\AppData\Local\Temp\STD5238_000000.tmp"
{txt}
{com}. *****************
. * Load the data *
. *****************
. use "\\adsroot.itcs.umich.edu\home\hursre\Documents\Papers\Evangelical Signalling\Paper Drafts\JOP\Replication_Material\Data.dta", clear
{txt}
{com}. 
. ***************
. ***************
. *** FIGURES ***
. ***************
. ***************
. ****************************************************************************************************************************
. * Figure 1: Across all candidates, Evangelical ballot name use rates by elecoral rule and municipal religions demographics *
. **************************************************************************************************************************** 
. quietly reg title i.bins##prop 
{txt}
{com}. quietly margins i.bins, over (i.prop)
{txt}
{com}. marginsplot, graphregion(color(white)) xsize(7) plot2opts(lpattern("--")) ///
>                          legend(order(1 "Mayoral Race" 2 "City Council Race") ) ///
>                          ytitle("Probability of Using Evangelical Ballot Name") ///
>                          xlabel( 0 "<10%" 1 "10%-15%" 2 "15%-20%" 3 "20%-25%" 4 "25%-30%" 5 ">30%" ) ///
>                          xtitle("Evangelical Share of Population") ///
>                          title("")  
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: bins prop{p_end}
{res}{txt}
{com}. *******************************************************************************************************************************
. * Figure 2: Across clergy candidates, Evangelical ballot name use rates by elecoral rule and municipal religions demographics *
. ******************************************************************************************************************************* 
. quietly reg title i.bins##prop if reljob==1
{txt}
{com}. quietly margins i.bins, over (i.prop)
{txt}
{com}. marginsplot, graphregion(color(white)) xsize(7) plot2opts(lpattern("--")) ///
>                          legend( order(1 "Mayoral Race" 2 "City Council Race") ) ///
>                          ytitle("Probability of Using Evangelical Ballot Name") ///
>                          xlabel( 0 "<10%" 1 "10%-15%" 2 "15%-20%" 3 "20%-25%" 4 "25%-30%" 5 ">30%" ) ///
>                          xtitle("Evangelical Share of Population") ///
>                          title("")
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: bins prop{p_end}
{res}{txt}
{com}. *********************************************
. * Figure 3: Visual summary of main results *
. ********************************************
. * For Evangelical title
. qui reghdfe title  prop anyevan interaction i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{txt}
{com}. qui margins, at(interaction=(.00(.025).45)) 
{txt}
{com}. marginsplot, title("") xtitle("Fraction of Municipal Population that is Evangelical") yscale(r(0 .01)) ylabel(0 (.002) .01)  /// 
> ytitle("Difference in Predicted Probability of Using Evangelical Ballot Name", size(small)) ///
> xlabel(0(.1).40) graphregion(color(white)) xsize(7) plotopts(color(black)) ciopts(color(black))
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: interaction{p_end}
{res}{txt}
{com}. ************************************************************************************
. * Figure 4: Relationship between evangelical ballot name use and electoral victory *
. ************************************************************************************
. quietly reghdfe win title##prop c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{txt}
{com}. quietly margins prop, at(title=(0(1)1))
{txt}
{com}. marginsplot, graphregion(color(white)) xsize(7) plot2opts(lpattern("--")) ///
>                          ytitle("Predicted Probability of Victory") ///
>                          xlabel( 0 "No" 1 "Yes") ///
>                          xtitle("Evangelical Ballot Name?") ///
>                          title("")
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: title prop{p_end}
{res}{txt}
{com}. *******************************************************************
. ** Figure A1: Winning threshold faced by City Council Candidates *
. ******************************************************************
. histogram thresh, freq xtitle("Electoral Quota") name(rel, replace) ytitle("Number of Candidates") ///
>                                    ylabel(200000 "200K" 400000 "400k" 600000 "600k" 800000 "800k") ///
>                                                                    graphregion(color(white)) xsize(7) color(black)                                         
{txt}(bin={res}63{txt}, start={res}.01818182{txt}, width={res}.00147507{txt})
{res}{txt}
{com}. ********************************************************
. ** Figure A2: Vote share captured by victorious mayors *
. ********************************************************
. * Note this requires loading in a different dataset, so I use preserve and then restore
. preserve
{txt}
{com}. import delimited "\\adsroot.itcs.umich.edu\home\hursre\Documents\Papers\Evangelical Signalling\Data\RawData\mayoral_share.csv", clear 
{res}{text}(9 vars, 27,421 obs)

{com}. * Turning variables to numeric
. gen share_turnout_n = real(share_turnout)
{txt}(31 missing values generated)

{com}. drop share_turnout
{txt}
{com}. rename share_turnout_n share_turnout
{res}{txt}
{com}. * Creating
. 
. histogram share_turnout, freq xtitle("Vote Share Captured by Victorious Mayor") ytitle("Number of Victorious Mayors") ///
>                                    graphregion(color(white)) xsize(7) color(black)
{txt}(bin={res}44{txt}, start={res}.01405489{txt}, width={res}.02084627{txt})
{res}{txt}
{com}. restore                                                                                                                                            
{txt}
{com}. ************************************************************
. ** Figure A3: Religious demographics across municipalities *
. ************************************************************
. histogram anyevan, freq xtitle("Evangelical Share of Municipal Population") name(rel, replace) ytitle("Number of Candidates") ///
>                                    ylabel(0 "0" 25000 "25k" 50000 "50k" 75000 "75k" 100000 "100k" 125000 "150k") ///
>                                    graphregion(color(white)) xsize(7) color(black)
{txt}(bin={res}63{txt}, start={res}.00041203{txt}, width={res}.01361635{txt})
{res}{txt}
{com}. *********************************************************************************
. * Figure A4: Visual summary of results using doctor ballot name placebo outcome *
. *********************************************************************************
. * Doctor Ballot name
. qui reghdfe doctor prop anyevan interaction i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{txt}
{com}. qui margins, at(interaction=(.00(.025).45)) 
{txt}
{com}. marginsplot, title("") xtitle("Fraction of Municipal Population that is Evangelical") yscale(r(0 .01)) ylabel(0 (.002) .01)  /// 
> ytitle("Difference in Predicted Probability of Using a Doctor Ballot Name", size(small)) ///
> xlabel(0(.1).40) graphregion(color(white)) xsize(7) plotopts(color(black)) ciopts(color(black))
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: interaction{p_end}
{res}{txt}
{com}. ***********************************************************************************
. * Figure A5: Visual summary of results using Catholic ballot name placebo outcome *
. ***********************************************************************************
. quietly reghdfe cath prop anyevan interaction i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{txt}
{com}. quietly margins, at(interaction=(.00(.025).45)) 
{txt}
{com}. marginsplot, title("") xtitle("Fraction of Municipal Population that is Evangelical") yscale(r(0 .01)) ylabel(0 (.002) .01)  /// 
> ytitle("Difference in Predicted Probability of Using Catholic Ballot Name", size(small)) ///
> xlabel(0(.1).40) graphregion(color(white)) xsize(7) plotopts(color(black))  ciopts(color(black))
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: interaction{p_end}
{res}{txt}
{com}. 
. 
. **************
. **************
. *** TABLES ***
. **************
. **************
. ***************************************************************
. * Table 1: Shows examples of different legal and ballot names *
. ***************************************************************
. * Each of the 6 lines below will show the candidate's information included in Table 1. Note that
. * the "Occupation" row is translated from Portuguese.
. * Row 1
. browse ballot name prop jobname if name=="TEREZINHA MARTINS DE SOUZA" & year==2000
{txt}
{com}. * Row 2
. browse ballot name prop jobname if name=="GUMERCINDO  OLIVEIRA DA SILVA" & year==2004
{txt}
{com}. * Row 3
. browse ballot name prop jobname if ballot=="DOUTORA  LICIA" & year==2000
{txt}
{com}. * Row 4
. browse ballot name prop jobname if name=="RUBENS ESTEVES ROQUE" & year==2016
{txt}
{com}. * Row 5
. browse ballot name prop jobname if name=="VALDIR MEIRELES DE OLIVEIRA" & year==2004
{txt}
{com}. * Row 6
. browse ballot name year prop jobname if ballot=="CILENE AQUINO" & year==2016
{txt}
{com}. *************************************************************************
. * Table 2: Shows use of evangelical ballot names across all candidates *
. *************************************************************************
. eststo clear
{txt}
{com}. * Model 1
. eststo: qui reghdfe title i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{txt}({res}est1{txt} stored)

{com}. * Model 2
. eststo: qui reghdfe title i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{txt}({res}est2{txt} stored)

{com}. * Model 3
. eststo: qui reghdfe title i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode_year) vce(cluster lcode)
{txt}({res}est3{txt} stored)

{com}. * Model 4 
. eststo: qui reghdfe title i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode_year) vce(cluster lcode)
{txt}({res}est4{txt} stored)

{com}. * Model 5
. eststo: qui reghdfe title i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(candidateid) vce(cluster lcode)
{txt}({res}est5{txt} stored)

{com}. * Model 6 
. eststo: qui reghdfe title i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(candidateid) vce(cluster lcode)
{txt}({res}est6{txt} stored)

{com}. * Prints full table
. esttab, keep (*prop anyevan *prop#c.anyevan) b(%5.4f) constant nobaselevels  se replace 
{res}
{txt}{hline 108}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)   
{txt}                    title           title           title           title           title           title   
{txt}{hline 108}
{txt}1.prop      {res}       0.0011***       0.0027***       0.0008***       0.0025***       0.0005          0.0001   {txt}
            {res} {ralign 12:{txt:(}0.0002{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}    {ralign 12:{txt:(}0.0002{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}   {txt}

{txt}anyevan     {res}       0.0236***       0.0334***       0.0000          0.0000          0.0186***       0.0165***{txt}
            {res} {ralign 12:{txt:(}0.0031{txt:)}}    {ralign 12:{txt:(}0.0038{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}0.0033{txt:)}}    {ralign 12:{txt:(}0.0038{txt:)}}   {txt}

{txt}1.prop#c.a~n{res}                      -0.0106***                      -0.0124***                       0.0024   {txt}
            {res}                 {ralign 12:{txt:(}0.0024{txt:)}}                    {ralign 12:{txt:(}0.0025{txt:)}}                    {ralign 12:{txt:(}0.0024{txt:)}}   {txt}
{txt}{hline 108}
{txt}N           {res}      2091796         2091796         2091784         2091784          767191          767191   {txt}
{txt}{hline 108}
{txt}Standard errors in parentheses
{txt}* p<0.05, ** p<0.01, *** p<0.001

{com}. ****************************************************
. * Table 3: Sensitivity analysis using Oster bounds *
. ****************************************************
. * Note that I run the baseline mode using xtreg rather than reghdfe because the psacalc package (used to calculate Oster's delta) does not recognize the reghdfe 
. * ouput as a linear model. The esimates using xtreg and reghdfe, however, are identiical, which can be easily verified by comparing the output below to the ouput from Model 1
. * in Table 2 estiamted above.
. *
. * Also note that, in the paper, I round the estimates of \delta reported below to four decimal places to maintain consistency with the rest of the paper.
. xtset lcode
{txt}{col 8}panel variable:  {res}lcode (unbalanced)
{txt}
{com}. xtreg title prop i.eduname i.female numcandidates i.year reljob timerun population anyevan pdt psdb pt ptb pmdb, fe cluster(lcode)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res} 2,091,796
{txt}Group variable: {res}lcode{txt}{col 49}Number of groups{col 67}={col 69}{res}     5,568

{txt}R-sq:{col 49}Obs per group:
     within  = {res}0.1213{col 63}{txt}min{col 67}={col 69}{res}        66
{txt}     between = {res}0.2561{col 63}{txt}avg{col 67}={col 69}{res}     375.7
{txt}     overall = {res}0.1225{col 63}{txt}max{col 67}={col 69}{res}     6,904

{txt}{col 49}F({res}20{txt},{res}5567{txt}){col 67}={col 70}{res}   327.74
{txt}corr(u_i, Xb){col 16}= {res}0.0034{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. Err. adjusted for {res:5,568} clusters in lcode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}        title{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}prop {c |}{col 15}{res}{space 2} .0010818{col 27}{space 2} .0002208{col 38}{space 1}    4.90{col 47}{space 3}0.000{col 55}{space 4} .0006489{col 68}{space 3} .0015148
{txt}{space 13} {c |}
{space 6}eduname {c |}
{space 11}2  {c |}{col 15}{res}{space 2} .0009115{col 27}{space 2} .0001983{col 38}{space 1}    4.60{col 47}{space 3}0.000{col 55}{space 4} .0005228{col 68}{space 3} .0013002
{txt}{space 11}3  {c |}{col 15}{res}{space 2}-.0004325{col 27}{space 2} .0001704{col 38}{space 1}   -2.54{col 47}{space 3}0.011{col 55}{space 4}-.0007667{col 68}{space 3}-.0000984
{txt}{space 11}4  {c |}{col 15}{res}{space 2}-.0023465{col 27}{space 2} .0001756{col 38}{space 1}  -13.36{col 47}{space 3}0.000{col 55}{space 4}-.0026907{col 68}{space 3}-.0020022
{txt}{space 9}999  {c |}{col 15}{res}{space 2}-.0002692{col 27}{space 2} .0004861{col 38}{space 1}   -0.55{col 47}{space 3}0.580{col 55}{space 4}-.0012222{col 68}{space 3} .0006839
{txt}{space 13} {c |}
{space 5}1.female {c |}{col 15}{res}{space 2} -.002957{col 27}{space 2} .0001359{col 38}{space 1}  -21.76{col 47}{space 3}0.000{col 55}{space 4}-.0032234{col 68}{space 3}-.0026906
{txt}numcandidates {c |}{col 15}{res}{space 2}-1.77e-06{col 27}{space 2} 1.42e-06{col 38}{space 1}   -1.25{col 47}{space 3}0.213{col 55}{space 4}-4.56e-06{col 68}{space 3} 1.01e-06
{txt}{space 13} {c |}
{space 9}year {c |}
{space 8}2004  {c |}{col 15}{res}{space 2} .0026729{col 27}{space 2} .0001555{col 38}{space 1}   17.19{col 47}{space 3}0.000{col 55}{space 4} .0023681{col 68}{space 3} .0029777
{txt}{space 8}2008  {c |}{col 15}{res}{space 2} .0023053{col 27}{space 2}  .000224{col 38}{space 1}   10.29{col 47}{space 3}0.000{col 55}{space 4} .0018662{col 68}{space 3} .0027445
{txt}{space 8}2012  {c |}{col 15}{res}{space 2} .0025161{col 27}{space 2} .0002343{col 38}{space 1}   10.74{col 47}{space 3}0.000{col 55}{space 4} .0020567{col 68}{space 3} .0029755
{txt}{space 8}2016  {c |}{col 15}{res}{space 2} .0045159{col 27}{space 2} .0002409{col 38}{space 1}   18.74{col 47}{space 3}0.000{col 55}{space 4} .0040435{col 68}{space 3} .0049882
{txt}{space 13} {c |}
{space 7}reljob {c |}{col 15}{res}{space 2} .6053263{col 27}{space 2} .0089358{col 38}{space 1}   67.74{col 47}{space 3}0.000{col 55}{space 4} .5878088{col 68}{space 3} .6228439
{txt}{space 6}timerun {c |}{col 15}{res}{space 2}-.0011597{col 27}{space 2} .0000856{col 38}{space 1}  -13.55{col 47}{space 3}0.000{col 55}{space 4}-.0013275{col 68}{space 3}-.0009919
{txt}{space 3}population {c |}{col 15}{res}{space 2} 2.04e-06{col 27}{space 2} 3.23e-06{col 38}{space 1}    0.63{col 47}{space 3}0.527{col 55}{space 4}-4.29e-06{col 68}{space 3} 8.37e-06
{txt}{space 6}anyevan {c |}{col 15}{res}{space 2} .0236174{col 27}{space 2} .0030526{col 38}{space 1}    7.74{col 47}{space 3}0.000{col 55}{space 4} .0176332{col 68}{space 3} .0296017
{txt}{space 10}pdt {c |}{col 15}{res}{space 2}-.0007988{col 27}{space 2} .0002319{col 38}{space 1}   -3.44{col 47}{space 3}0.001{col 55}{space 4}-.0012533{col 68}{space 3}-.0003442
{txt}{space 9}psdb {c |}{col 15}{res}{space 2}-.0010787{col 27}{space 2} .0001814{col 38}{space 1}   -5.95{col 47}{space 3}0.000{col 55}{space 4}-.0014342{col 68}{space 3}-.0007231
{txt}{space 11}pt {c |}{col 15}{res}{space 2}-.0028508{col 27}{space 2} .0001729{col 38}{space 1}  -16.49{col 47}{space 3}0.000{col 55}{space 4}-.0031896{col 68}{space 3}-.0025119
{txt}{space 10}ptb {c |}{col 15}{res}{space 2} .0000576{col 27}{space 2} .0002474{col 38}{space 1}    0.23{col 47}{space 3}0.816{col 55}{space 4}-.0004274{col 68}{space 3} .0005426
{txt}{space 9}pmdb {c |}{col 15}{res}{space 2}-.0012711{col 27}{space 2} .0001507{col 38}{space 1}   -8.43{col 47}{space 3}0.000{col 55}{space 4}-.0015665{col 68}{space 3}-.0009757
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .0003279{col 27}{space 2} .0006251{col 38}{space 1}    0.52{col 47}{space 3}0.600{col 55}{space 4}-.0008975{col 68}{space 3} .0015533
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
      sigma_u {c |} {res} .00450723
      {txt}sigma_e {c |} {res} .07235035
          {txt}rho {c |} {res} .00386594{txt}   (fraction of variance due to u_i)
{hline 14}{c BT}{hline 64}

{com}. * Estimate for R_{c -(}Max{c )-}=R_{c -(}Model{c )-}
. psacalc delta prop, rmax(.1225)
{res}
{txt}{col 18}{hline 4} Bound Estimate {hline 4}
{col 1}{hline 13}{c +}{hline 64}
{col 1}delta{col 14}{c |}{col 18}{res}-2396.51457
{txt}{col 1}{hline 13}{c +}{hline 64}

{col 18}{hline 4} Inputs from Regressions {hline 4}
{col 14}{c |}{col 21}Coeff.{col 49}R-Squared
{col 1}{hline 13}{c +}{hline 64}
{col 1}Uncontrolled{col 14}{c |}{col 18}{res}     0.00085{col 49}0.000
{col 1}{txt}Controlled{col 14}{c |}{col 18}{res}     0.00108{col 49}0.121
{col 1}{txt}{hline 13}{c +}{hline 64}

{col 18}{hline 4} Other Inputs {hline 4}
{col 1}{hline 13}{c +}{hline 64}
{col 1}R_max{col 14}{c |}{col 18}0.122
{col 1}Beta{col 14}{c |}{col 18} 0.000000
{col 1}Unr. Controls{col 14}{c |}{col 18}
{col 1}{hline 13}{c +}{hline 64}

{com}. * Estimate for R_{c -(}Max{c )-}=1.3 X R_{c -(}Model{c )-}
. psacalc delta prop, rmax(0.15925)
{res}
{txt}{col 18}{hline 4} Bound Estimate {hline 4}
{col 1}{hline 13}{c +}{hline 64}
{col 1}delta{col 14}{c |}{col 18}{res}  -79.14110
{txt}{col 1}{hline 13}{c +}{hline 64}

{col 18}{hline 4} Inputs from Regressions {hline 4}
{col 14}{c |}{col 21}Coeff.{col 49}R-Squared
{col 1}{hline 13}{c +}{hline 64}
{col 1}Uncontrolled{col 14}{c |}{col 18}{res}     0.00085{col 49}0.000
{col 1}{txt}Controlled{col 14}{c |}{col 18}{res}     0.00108{col 49}0.121
{col 1}{txt}{hline 13}{c +}{hline 64}

{col 18}{hline 4} Other Inputs {hline 4}
{col 1}{hline 13}{c +}{hline 64}
{col 1}R_max{col 14}{c |}{col 18}0.159
{col 1}Beta{col 14}{c |}{col 18} 0.000000
{col 1}Unr. Controls{col 14}{c |}{col 18}
{col 1}{hline 13}{c +}{hline 64}

{com}. * Estimate for R_{c -(}Max{c )-}=1
. psacalc delta prop, rmax(1)
{res}
{txt}{col 18}{hline 4} Bound Estimate {hline 4}
{col 1}{hline 13}{c +}{hline 64}
{col 1}delta{col 14}{c |}{col 18}{res}   -3.42275
{txt}{col 1}{hline 13}{c +}{hline 64}

{col 18}{hline 4} Inputs from Regressions {hline 4}
{col 14}{c |}{col 21}Coeff.{col 49}R-Squared
{col 1}{hline 13}{c +}{hline 64}
{col 1}Uncontrolled{col 14}{c |}{col 18}{res}     0.00085{col 49}0.000
{col 1}{txt}Controlled{col 14}{c |}{col 18}{res}     0.00108{col 49}0.121
{col 1}{txt}{hline 13}{c +}{hline 64}

{col 18}{hline 4} Other Inputs {hline 4}
{col 1}{hline 13}{c +}{hline 64}
{col 1}R_max{col 14}{c |}{col 18}1.000
{col 1}Beta{col 14}{c |}{col 18} 0.000000
{col 1}Unr. Controls{col 14}{c |}{col 18}
{col 1}{hline 13}{c +}{hline 64}

{com}. *******************************
. * Table A1: Summary statistics*
. *******************************
. qui eststo all: estpost tabstat title prop doctor mayor vicemayor female reljob edu1 edu2 edu3 edu4 pdt psdb pt ptb pmdb other population anyevan, statistics(mean sd min max) columns(statistics) 
{txt}
{com}. esttab all, cells("mean(fmt(%5.3f)) sd(fmt(%5.3f)) min(fmt(%5.2f)) max(fmt(%5.2f))")   label replace
{res}
{txt}{hline 72}
{txt}                              (1)                                       
{txt}                                                                        
{txt}                             mean           sd          min          max
{txt}{hline 72}
{txt}title               {res}        0.006        0.077         0.00         1.00{txt}
{txt}prop                {res}        0.932        0.253         0.00         1.00{txt}
{txt}doctor              {res}        0.001        0.032         0.00         1.00{txt}
{txt}mayor               {res}        0.038        0.190         0.00         1.00{txt}
{txt}vicemayor           {res}        0.031        0.173         0.00         1.00{txt}
{txt}Female              {res}        0.250        0.433         0.00         1.00{txt}
{txt}reljob              {res}        0.002        0.044         0.00         1.00{txt}
{txt}Less than Middle S~l{res}        0.333        0.471         0.00         1.00{txt}
{txt}Middle School       {res}        0.160        0.367         0.00         1.00{txt}
{txt}edu3                {res}        0.274        0.446         0.00         1.00{txt}
{txt}Some College        {res}        0.224        0.417         0.00         1.00{txt}
{txt}Democratic Labor P~y{res}        0.061        0.239         0.00         1.00{txt}
{txt}Brazilian Social D~y{res}        0.086        0.281         0.00         1.00{txt}
{txt}Workers Party       {res}        0.079        0.270         0.00         1.00{txt}
{txt}Brazilian Labor Pa~y{res}        0.063        0.242         0.00         1.00{txt}
{txt}Brazilian Democrat~a{res}        0.110        0.312         0.00         1.00{txt}
{txt}Other Party         {res}        0.601        0.490         0.00         1.00{txt}
{txt}population          {res}      153.634      722.451         0.80     11253.50{txt}
{txt}anyevan             {res}        0.177        0.094         0.00         0.86{txt}
{txt}{hline 72}
{txt}Observations        {res}      2091796                                       {txt}
{txt}{hline 72}

{com}. ***************************
. * Table A2: See manuscript*
. ***************************
. ****************************************************************************
. * Table A3: Shows use of evangelical ballot names across clergy candidates *
. ****************************************************************************
. eststo clear
{txt}
{com}. * Model 7
. eststo: qui reghdfe title i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb if reljob==1, absorb(lcode) vce(cluster lcode)
{txt}({res}est1{txt} stored)

{com}. * Model 8
. eststo: qui reghdfe title i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb if reljob==1, absorb(lcode) vce(cluster lcode)
{txt}({res}est2{txt} stored)

{com}. * Model 9
. eststo: qui reghdfe title i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb if reljob==1, absorb(lcode_year) vce(cluster lcode)
{txt}({res}est3{txt} stored)

{com}. * Model 10
. eststo: qui reghdfe title i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb if reljob==1, absorb(lcode_year) vce(cluster lcode)
{txt}({res}est4{txt} stored)

{com}. * Model 11
. eststo: qui reghdfe title i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb if everreljob==1, absorb(candidateid) vce(cluster lcode)
{txt}({res}est5{txt} stored)

{com}. * Model 12
. eststo: reghdfe title i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb if everreljob==1, absorb(candidateid) vce(cluster lcode)
{res}{txt}(dropped 2956 {browse "http://scorreia.com/research/singletons.pdf":singleton observations})
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}{txt}warning: missing F statistic; dropped variables due to collinearity or too few clusters
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,444
{txt}Absorbing 1 HDFE group{col 51}{help j_robustsingular##|_new:F(  21,    706)}{col 67}=          {res}.
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}=          {res}.
{txt}{col 51}R-squared{col 67}= {res}    0.7855
{txt}{col 51}Adj R-squared{col 67}= {res}    0.6284
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0381
{txt}{col 1}Number of clusters ({res}lcode{txt}) {col 30}= {res}       707{txt}{col 51}Root MSE{col 67}= {res}    0.3041

{txt}{ralign 80:(Std. Err. adjusted for {res:707} clusters in lcode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}         title{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.prop {c |}{col 16}{res}{space 2} .1755492{col 28}{space 2} .1153458{col 39}{space 1}    1.52{col 48}{space 3}0.128{col 56}{space 4}-.0509126{col 69}{space 3}  .402011
{txt}{space 7}anyevan {c |}{col 16}{res}{space 2} 1.258536{col 28}{space 2} .7284941{col 39}{space 1}    1.73{col 48}{space 3}0.084{col 56}{space 4}-.1717377{col 69}{space 3} 2.688811
{txt}{space 14} {c |}
prop#c.anyevan {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.3315116{col 28}{space 2} .4813493{col 39}{space 1}   -0.69{col 48}{space 3}0.491{col 56}{space 4}-1.276559{col 69}{space 3} .6135359
{txt}{space 14} {c |}
{space 7}eduname {c |}
{space 12}2  {c |}{col 16}{res}{space 2}-.0309449{col 28}{space 2} .0334647{col 39}{space 1}   -0.92{col 48}{space 3}0.355{col 56}{space 4}-.0966471{col 69}{space 3} .0347572
{txt}{space 12}3  {c |}{col 16}{res}{space 2}-.0466378{col 28}{space 2} .0378091{col 39}{space 1}   -1.23{col 48}{space 3}0.218{col 56}{space 4}-.1208696{col 69}{space 3}  .027594
{txt}{space 12}4  {c |}{col 16}{res}{space 2}-.0289591{col 28}{space 2} .0437704{col 39}{space 1}   -0.66{col 48}{space 3}0.508{col 56}{space 4}-.1148947{col 69}{space 3} .0569765
{txt}{space 10}999  {c |}{col 16}{res}{space 2} .0217561{col 28}{space 2} .1356933{col 39}{space 1}    0.16{col 48}{space 3}0.873{col 56}{space 4}-.2446545{col 69}{space 3} .2881668
{txt}{space 14} {c |}
{space 6}1.female {c |}{col 16}{res}{space 2}-.5927315{col 28}{space 2} .0537645{col 39}{space 1}  -11.02{col 48}{space 3}0.000{col 56}{space 4}-.6982889{col 69}{space 3}-.4871742
{txt}{space 1}numcandidates {c |}{col 16}{res}{space 2} -.000258{col 28}{space 2} .0001223{col 39}{space 1}   -2.11{col 48}{space 3}0.035{col 56}{space 4}-.0004981{col 69}{space 3} -.000018
{txt}{space 14} {c |}
{space 10}year {c |}
{space 9}2004  {c |}{col 16}{res}{space 2} .0610886{col 28}{space 2} .0386351{col 39}{space 1}    1.58{col 48}{space 3}0.114{col 56}{space 4}-.0147648{col 69}{space 3}  .136942
{txt}{space 9}2008  {c |}{col 16}{res}{space 2} .0214605{col 28}{space 2} .0744238{col 39}{space 1}    0.29{col 48}{space 3}0.773{col 56}{space 4} -.124658{col 69}{space 3}  .167579
{txt}{space 9}2012  {c |}{col 16}{res}{space 2} .0332451{col 28}{space 2} .0906906{col 39}{space 1}    0.37{col 48}{space 3}0.714{col 56}{space 4}-.1448104{col 69}{space 3} .2113007
{txt}{space 9}2016  {c |}{col 16}{res}{space 2} .0399242{col 28}{space 2} .1120959{col 39}{space 1}    0.36{col 48}{space 3}0.722{col 56}{space 4}-.1801569{col 69}{space 3} .2600054
{txt}{space 14} {c |}
{space 8}reljob {c |}{col 16}{res}{space 2} .0626779{col 28}{space 2} .0151358{col 39}{space 1}    4.14{col 48}{space 3}0.000{col 56}{space 4} .0329614{col 69}{space 3} .0923944
{txt}{space 7}timerun {c |}{col 16}{res}{space 2} .0098109{col 28}{space 2}  .029014{col 39}{space 1}    0.34{col 48}{space 3}0.735{col 56}{space 4}-.0471531{col 69}{space 3} .0667749
{txt}{space 4}population {c |}{col 16}{res}{space 2}-.0005147{col 28}{space 2}  .000191{col 39}{space 1}   -2.70{col 48}{space 3}0.007{col 56}{space 4}-.0008897{col 69}{space 3}-.0001398
{txt}{space 11}pdt {c |}{col 16}{res}{space 2} .0264102{col 28}{space 2} .0442217{col 39}{space 1}    0.60{col 48}{space 3}0.551{col 56}{space 4}-.0604117{col 69}{space 3}  .113232
{txt}{space 10}psdb {c |}{col 16}{res}{space 2}-.0749134{col 28}{space 2}  .039432{col 39}{space 1}   -1.90{col 48}{space 3}0.058{col 56}{space 4}-.1523314{col 69}{space 3} .0025045
{txt}{space 12}pt {c |}{col 16}{res}{space 2}-.0668002{col 28}{space 2} .0696957{col 39}{space 1}   -0.96{col 48}{space 3}0.338{col 56}{space 4}-.2036358{col 69}{space 3} .0700355
{txt}{space 11}ptb {c |}{col 16}{res}{space 2}-.0497623{col 28}{space 2} .0433579{col 39}{space 1}   -1.15{col 48}{space 3}0.251{col 56}{space 4}-.1348883{col 69}{space 3} .0353636
{txt}{space 10}pmdb {c |}{col 16}{res}{space 2}-.0111338{col 28}{space 2} .0394677{col 39}{space 1}   -0.28{col 48}{space 3}0.778{col 56}{space 4}-.0886219{col 69}{space 3} .0663542
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .4090551{col 28}{space 2}  .153733{col 39}{space 1}    2.66{col 48}{space 3}0.008{col 56}{space 4} .1072266{col 69}{space 3} .7108837
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} candidateid{col 14}{c |}{space 1}     1013{col 27}{space 1}     1013{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est6{txt} stored)

{com}. * Prints full table
. esttab, keep (*prop anyevan *prop#c.anyevan) b(%5.4f) constant nobaselevels  se replace 
{res}
{txt}{hline 108}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)   
{txt}                    title           title           title           title           title           title   
{txt}{hline 108}
{txt}1.prop      {res}       0.2205***       0.3590**        0.1535          0.2897          0.1043          0.1755   {txt}
            {res} {ralign 12:{txt:(}0.0553{txt:)}}    {ralign 12:{txt:(}0.1178{txt:)}}    {ralign 12:{txt:(}0.0932{txt:)}}    {ralign 12:{txt:(}0.2092{txt:)}}    {ralign 12:{txt:(}0.0556{txt:)}}    {ralign 12:{txt:(}0.1153{txt:)}}   {txt}

{txt}anyevan     {res}      -0.4463          0.1821          0.0000          0.0000          0.9809          1.2585   {txt}
            {res} {ralign 12:{txt:(}0.6733{txt:)}}    {ralign 12:{txt:(}0.8699{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}0.5946{txt:)}}    {ralign 12:{txt:(}0.7285{txt:)}}   {txt}

{txt}1.prop#c.a~n{res}                      -0.6127                         -0.5439                         -0.3315   {txt}
            {res}                 {ralign 12:{txt:(}0.4717{txt:)}}                    {ralign 12:{txt:(}0.7324{txt:)}}                    {ralign 12:{txt:(}0.4813{txt:)}}   {txt}
{txt}{hline 108}
{txt}N           {res}         3181            3181            1931            1931            2444            2444   {txt}
{txt}{hline 108}
{txt}Standard errors in parentheses
{txt}* p<0.05, ** p<0.01, *** p<0.001

{com}. ***********************************************************
. * Table A4: Shows use of doctor names among all candidates*
. ***********************************************************
. eststo clear
{txt}
{com}. * Model 13
. eststo: qui reghdfe doctor i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{txt}({res}est1{txt} stored)

{com}. * Model 14
. eststo: qui reghdfe doctor i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{txt}({res}est2{txt} stored)

{com}. * Model 15
. eststo: qui reghdfe doctor i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode_year) vce(cluster lcode)
{txt}({res}est3{txt} stored)

{com}. * Model 16 
. eststo: qui reghdfe doctor i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode_year) vce(cluster lcode)
{txt}({res}est4{txt} stored)

{com}. * Model 17
. eststo: qui reghdfe doctor i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(candidateid) vce(cluster lcode)
{txt}({res}est5{txt} stored)

{com}. * Model 18 
. eststo: qui reghdfe doctor i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(candidateid) vce(cluster lcode)
{txt}({res}est6{txt} stored)

{com}. * Prints full table
. esttab, keep (*prop anyevan *prop#c.anyevan) b(%5.4f) constant nobaselevels  se replace 
{res}
{txt}{hline 108}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)   
{txt}                   doctor          doctor          doctor          doctor          doctor          doctor   
{txt}{hline 108}
{txt}1.prop      {res}      -0.0011***      -0.0018***      -0.0013***      -0.0018***       0.0002         -0.0005   {txt}
            {res} {ralign 12:{txt:(}0.0002{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}    {ralign 12:{txt:(}0.0002{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}    {ralign 12:{txt:(}0.0005{txt:)}}   {txt}

{txt}anyevan     {res}      -0.0020         -0.0058**        0.0000          0.0000         -0.0021         -0.0061   {txt}
            {res} {ralign 12:{txt:(}0.0014{txt:)}}    {ralign 12:{txt:(}0.0020{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}0.0026{txt:)}}    {ralign 12:{txt:(}0.0039{txt:)}}   {txt}

{txt}1.prop#c.a~n{res}                       0.0041**                        0.0039*                         0.0046   {txt}
            {res}                 {ralign 12:{txt:(}0.0015{txt:)}}                    {ralign 12:{txt:(}0.0015{txt:)}}                    {ralign 12:{txt:(}0.0028{txt:)}}   {txt}
{txt}{hline 108}
{txt}N           {res}      2091796         2091796         2091784         2091784          767191          767191   {txt}
{txt}{hline 108}
{txt}Standard errors in parentheses
{txt}* p<0.05, ** p<0.01, *** p<0.001

{com}. *************************************************************
. ** Table A5: Shows use of Catholic names among all candidate*
. *************************************************************
. eststo clear
{txt}
{com}. * Model 19
. eststo: qui reghdfe cath i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{txt}({res}est1{txt} stored)

{com}. * Model 20
. eststo: qui reghdfe cath i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{txt}({res}est2{txt} stored)

{com}. * Model 21
. eststo: qui reghdfe cath i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode_year) vce(cluster lcode)
{txt}({res}est3{txt} stored)

{com}. * Model 22
. eststo: qui reghdfe cath i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(lcode_year) vce(cluster lcode)
{txt}({res}est4{txt} stored)

{com}. * Model 23
. eststo: qui reghdfe cath i.prop anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(candidateid) vce(cluster lcode)
{txt}({res}est5{txt} stored)

{com}. * Model 24
. eststo: qui reghdfe cath i.prop##c.anyevan i.eduname i.female numcandidates i.year reljob timerun population pdt psdb pt ptb pmdb, absorb(candidateid) vce(cluster lcode)
{txt}({res}est6{txt} stored)

{com}. * Prints full table
. esttab, keep (*prop anyevan *prop#c.anyevan) b(%5.4f) constant nobaselevels  se replace 
{res}
{txt}{hline 108}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)   
{txt}                     cath            cath            cath            cath            cath            cath   
{txt}{hline 108}
{txt}1.prop      {res}      -0.0013***      -0.0021***      -0.0013***      -0.0020***       0.0000         -0.0000   {txt}
            {res} {ralign 12:{txt:(}0.0002{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}    {ralign 12:{txt:(}0.0002{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}    {ralign 12:{txt:(}0.0001{txt:)}}    {ralign 12:{txt:(}0.0001{txt:)}}   {txt}

{txt}anyevan     {res}       0.0004         -0.0039*         0.0000          0.0000         -0.0020         -0.0021   {txt}
            {res} {ralign 12:{txt:(}0.0009{txt:)}}    {ralign 12:{txt:(}0.0016{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}.{txt:)}}    {ralign 12:{txt:(}0.0011{txt:)}}    {ralign 12:{txt:(}0.0012{txt:)}}   {txt}

{txt}1.prop#c.a~n{res}                       0.0047***                       0.0051***                       0.0001   {txt}
            {res}                 {ralign 12:{txt:(}0.0014{txt:)}}                    {ralign 12:{txt:(}0.0014{txt:)}}                    {ralign 12:{txt:(}0.0006{txt:)}}   {txt}
{txt}{hline 108}
{txt}N           {res}      2091796         2091796         2091784         2091784          767191          767191   {txt}
{txt}{hline 108}
{txt}Standard errors in parentheses
{txt}* p<0.05, ** p<0.01, *** p<0.001

{com}. *********************************
. ** Table A6: Looking at victory *
. *********************************
. eststo clear
{txt}
{com}. * Model 25
. eststo: qui reg win i.title, cluster(lcode)
{txt}({res}est1{txt} stored)

{com}. * Model 26
. eststo: qui  reg win title##prop, cluster(lcode)
{txt}({res}est2{txt} stored)

{com}. * Model 27
. eststo: reghdfe win title##prop c.anyevan i.eduname i.female numcandidates i.year reljob timerun population anyevan pdt psdb pt ptb pmdb, absorb(lcode) vce(cluster lcode)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}{txt}note: anyevan omitted because of collinearity
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res} 2,091,796
{txt}Absorbing 1 HDFE group{col 51}F({res}  22{txt},{res}   5567{txt}){col 67}= {res}   2204.43
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0000
{txt}{col 51}R-squared{col 67}= {res}    0.1067
{txt}{col 51}Adj R-squared{col 67}= {res}    0.1043
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0556
{txt}{col 1}Number of clusters ({res}lcode{txt}) {col 30}= {res}     5,568{txt}{col 51}Root MSE{col 67}= {res}    0.3231

{txt}{ralign 79:(Std. Err. adjusted for {res:5,568} clusters in lcode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}          win{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}1.title {c |}{col 15}{res}{space 2}-.1070656{col 27}{space 2} .0113857{col 38}{space 1}   -9.40{col 47}{space 3}0.000{col 55}{space 4}-.1293859{col 68}{space 3}-.0847452
{txt}{space 7}1.prop {c |}{col 15}{res}{space 2}-.0462226{col 27}{space 2} .0016421{col 38}{space 1}  -28.15{col 47}{space 3}0.000{col 55}{space 4}-.0494418{col 68}{space 3}-.0430034
{txt}{space 13} {c |}
{space 3}title#prop {c |}
{space 9}1 1  {c |}{col 15}{res}{space 2} .0854101{col 27}{space 2} .0115061{col 38}{space 1}    7.42{col 47}{space 3}0.000{col 55}{space 4} .0628538{col 68}{space 3} .1079665
{txt}{space 13} {c |}
{space 6}anyevan {c |}{col 15}{res}{space 2} .1531875{col 27}{space 2}  .014541{col 38}{space 1}   10.53{col 47}{space 3}0.000{col 55}{space 4} .1246816{col 68}{space 3} .1816935
{txt}{space 13} {c |}
{space 6}eduname {c |}
{space 11}2  {c |}{col 15}{res}{space 2} .0271637{col 27}{space 2} .0007727{col 38}{space 1}   35.15{col 47}{space 3}0.000{col 55}{space 4} .0256489{col 68}{space 3} .0286785
{txt}{space 11}3  {c |}{col 15}{res}{space 2} .0456881{col 27}{space 2} .0008216{col 38}{space 1}   55.61{col 47}{space 3}0.000{col 55}{space 4} .0440775{col 68}{space 3} .0472988
{txt}{space 11}4  {c |}{col 15}{res}{space 2} .0909161{col 27}{space 2} .0011114{col 38}{space 1}   81.81{col 47}{space 3}0.000{col 55}{space 4} .0887374{col 68}{space 3} .0930949
{txt}{space 9}999  {c |}{col 15}{res}{space 2}  -.02171{col 27}{space 2} .0022646{col 38}{space 1}   -9.59{col 47}{space 3}0.000{col 55}{space 4}-.0261495{col 68}{space 3}-.0172704
{txt}{space 13} {c |}
{space 5}1.female {c |}{col 15}{res}{space 2}-.0746636{col 27}{space 2} .0009582{col 38}{space 1}  -77.92{col 47}{space 3}0.000{col 55}{space 4} -.076542{col 68}{space 3}-.0727851
{txt}numcandidates {c |}{col 15}{res}{space 2}-.0000734{col 27}{space 2}  .000019{col 38}{space 1}   -3.86{col 47}{space 3}0.000{col 55}{space 4}-.0001106{col 68}{space 3}-.0000362
{txt}{space 13} {c |}
{space 9}year {c |}
{space 8}2004  {c |}{col 15}{res}{space 2}-.0986507{col 27}{space 2} .0010118{col 38}{space 1}  -97.50{col 47}{space 3}0.000{col 55}{space 4}-.1006341{col 68}{space 3}-.0966672
{txt}{space 8}2008  {c |}{col 15}{res}{space 2}  -.12438{col 27}{space 2} .0015147{col 38}{space 1}  -82.11{col 47}{space 3}0.000{col 55}{space 4}-.1273494{col 68}{space 3}-.1214105
{txt}{space 8}2012  {c |}{col 15}{res}{space 2}-.0984916{col 27}{space 2} .0014071{col 38}{space 1}  -70.00{col 47}{space 3}0.000{col 55}{space 4}  -.10125{col 68}{space 3}-.0957331
{txt}{space 8}2016  {c |}{col 15}{res}{space 2}-.0938245{col 27}{space 2} .0012443{col 38}{space 1}  -75.41{col 47}{space 3}0.000{col 55}{space 4}-.0962637{col 68}{space 3}-.0913852
{txt}{space 13} {c |}
{space 7}reljob {c |}{col 15}{res}{space 2} .0158682{col 27}{space 2} .0052306{col 38}{space 1}    3.03{col 47}{space 3}0.002{col 55}{space 4} .0056141{col 68}{space 3} .0261223
{txt}{space 6}timerun {c |}{col 15}{res}{space 2} .0760313{col 27}{space 2} .0005994{col 38}{space 1}  126.84{col 47}{space 3}0.000{col 55}{space 4} .0748562{col 68}{space 3} .0772064
{txt}{space 3}population {c |}{col 15}{res}{space 2} .0000814{col 27}{space 2} .0000347{col 38}{space 1}    2.35{col 47}{space 3}0.019{col 55}{space 4} .0000134{col 68}{space 3} .0001494
{txt}{space 6}anyevan {c |}{col 15}{res}{space 2}        0{col 27}{txt}  (omitted)
{space 10}pdt {c |}{col 15}{res}{space 2} .0063507{col 27}{space 2} .0011619{col 38}{space 1}    5.47{col 47}{space 3}0.000{col 55}{space 4} .0040729{col 68}{space 3} .0086286
{txt}{space 9}psdb {c |}{col 15}{res}{space 2} .0305366{col 27}{space 2} .0011471{col 38}{space 1}   26.62{col 47}{space 3}0.000{col 55}{space 4} .0282878{col 68}{space 3} .0327853
{txt}{space 11}pt {c |}{col 15}{res}{space 2}-.0313452{col 27}{space 2} .0013867{col 38}{space 1}  -22.60{col 47}{space 3}0.000{col 55}{space 4}-.0340637{col 68}{space 3}-.0286266
{txt}{space 10}ptb {c |}{col 15}{res}{space 2} .0114691{col 27}{space 2} .0011539{col 38}{space 1}    9.94{col 47}{space 3}0.000{col 55}{space 4} .0092071{col 68}{space 3} .0137312
{txt}{space 9}pmdb {c |}{col 15}{res}{space 2} .0326343{col 27}{space 2} .0010632{col 38}{space 1}   30.69{col 47}{space 3}0.000{col 55}{space 4}   .03055{col 68}{space 3} .0347185
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .1092206{col 27}{space 2} .0042999{col 38}{space 1}   25.40{col 47}{space 3}0.000{col 55}{space 4} .1007911{col 68}{space 3} .1176502
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}       lcode{col 14}{c |}{space 1}     5568{col 27}{space 1}     5568{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est3{txt} stored)

{com}. * Prints full table
. esttab, keep (*title *prop) b(%5.4f) constant nobaselevels  se replace 
{res}
{txt}{hline 60}
{txt}                      (1)             (2)             (3)   
{txt}                      win             win             win   
{txt}{hline 60}
{txt}1.title     {res}      -0.0697***      -0.1838***      -0.1071***{txt}
            {res} {ralign 12:{txt:(}0.0032{txt:)}}    {ralign 12:{txt:(}0.0115{txt:)}}    {ralign 12:{txt:(}0.0114{txt:)}}   {txt}

{txt}1.prop      {res}                      -0.1307***      -0.0462***{txt}
            {res}                 {ralign 12:{txt:(}0.0008{txt:)}}    {ralign 12:{txt:(}0.0016{txt:)}}   {txt}

{txt}1.title#1.~p{res}                       0.1227***       0.0854***{txt}
            {res}                 {ralign 12:{txt:(}0.0118{txt:)}}    {ralign 12:{txt:(}0.0115{txt:)}}   {txt}
{txt}{hline 60}
{txt}N           {res}      2091796         2091796         2091796   {txt}
{txt}{hline 60}
{txt}Standard errors in parentheses
{txt}* p<0.05, ** p<0.01, *** p<0.001

{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}\\adsroot.itcs.umich.edu\home\hursre\Documents\Papers\Evangelical Signalling\Paper Drafts\JOP\Replication_Material\Log_of_Analysis_do.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}22 Aug 2022, 09:59:19
{txt}{.-}
{smcl}
{txt}{sf}{ul off}